CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Data Mining Analysis with Association Rules Method to Determine the Result of Fish Catch using FP-Growth Algorithm

by Dito Sukma Wijaya, Devi Fitrianah
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 15
Year of Publication: 2018
Authors: Dito Sukma Wijaya, Devi Fitrianah
10.5120/ijca2018917748

Dito Sukma Wijaya, Devi Fitrianah . Data Mining Analysis with Association Rules Method to Determine the Result of Fish Catch using FP-Growth Algorithm. International Journal of Computer Applications. 181, 15 ( Sep 2018), 7-15. DOI=10.5120/ijca2018917748

@article{ 10.5120/ijca2018917748,
author = { Dito Sukma Wijaya, Devi Fitrianah },
title = { Data Mining Analysis with Association Rules Method to Determine the Result of Fish Catch using FP-Growth Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 181 },
number = { 15 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 7-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number15/29896-2018917748/ },
doi = { 10.5120/ijca2018917748 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:06:02.752826+05:30
%A Dito Sukma Wijaya
%A Devi Fitrianah
%T Data Mining Analysis with Association Rules Method to Determine the Result of Fish Catch using FP-Growth Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 15
%P 7-15
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study aims to analyze the data to determine the correlation between fish catch, whether certain fish affect the other fish. Lots of natural resources in Indonesia, especially in the marine sector that can be used one of them are fisheries. Each region has the potential of marine fish species with different numbers and species, this can lead to problems that lack of information on the correlation between fish catch, whether certain fish potentially affect the catch. To overcome this problem, it is necessary to analyze the pattern of fish catch data using data mining technique. The method used is association rules with FP Growth algorithm. The method of association rules is used to analyze the data so as to produce data in the form of correlation pattern between fish catch. Thus, based on the analysis of fish data the higher the minimum support and minimum confidence used, the less frequent itemset and rules that is formed and decreases the accuracy. All rules generated in this study have a value of lift ratio of more than 1.00 so that it can be used as a reference in knowing the correlation between fish catch to optimize the fisheries results for the fishermen.

References
  1. R. Noviyanti, “Kondisi Perikanan Tangkap Di Wilayah Pengelolaan Perikanan (WPP) Indonesia,” Univ. Terbuka, Jakarta, no. Gambar 1, 2011.
  2. D. Listriani, A. H. Setyaningrum, and F. E. M. A, “PENERAPAN METODE ASOSIASI MENGGUNAKAN ALGORITMA APRIORI PADA APLIKASI ANALISA POLA BELANJA KONSUMEN ( Studi Kasus Toko Buku Gramedia Bintaro ),” J. Tek. Inform., vol. 9, no. 2, pp. 120–127, 2016.
  3. C. D. dan B. D. S. Novadyana S, “IMPLEMENTASI METODE ASSOCIATION RULE DENGAN ALGORITMA FP GROWTH PADA DATA HASIL TANGKAPAN IKAN LAUT,” Doro J., vol. 4, no. 11, 2014.
  4. D. Samuel, “Penerapan Stuktur FP-Tree dan Algoritma FP-Growth dalam Optimasi Penentuan Frequent Itemset,” p. 6, 2008.
  5. Erwin, “Analisis Market Basket Dengan Algoritma,” J. Generic, vol. 4, pp. 26–30, 2009.
  6. J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.
  7. G. Gunadi and D. I. Sensuse, “Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori Dan Frequent Pattern Growth ( Fp-Growth ) :,” Telematika, vol. 4, no. 1, pp. 118–132, 2012.
  8. Raorane AA, Kulkarni RV, and Jitkar BD, “Association Rule – Extracting Knowledge Using Market Basket Analysis,” Res. J. Recent Sci. Feb. Res.J.Recent Sci, vol. 1, no. 2, pp. 19–27, 2012.
  9. S. Gupta and R. Mamtora, “A Survey on Association Rule Mining in Market Basket Analysis,” Int. J. Inf. Comput. Technol., vol. 4, no. 4, pp. 409–414, 2014.
  10. M. Fauzy, K. R. Saleh W, and I. Asror, “Penerapan Metode Association Rule Menggunakan Algoritma Apriori Pada Simulasi Prediksi Hujan Wilayah Kota Bandung,” J. Ilm. Teknol. Inf. Terap., vol. 13, no. 2, pp. 115–124, 2014.
  11. D. G. Bengen, “Pelatihan Pengelolaan Wilayah Pesisir Terpadu,” Pengelolaan Wil. Pesisir Terpadu, no. November, pp. 1–167, 2001.
  12. H. E. Irianto and I. Soesilo, “Dukungan Teknologi Penyediaan Produk Perikanan,” J. Chem. Inf. Model., vol. 53, pp. 1689–1699, 2013.
  13. D. I. Perairan et al., “Laporan penelitian komposisi jenis alat tangkap yang beroperasi di perairan teluk banten, serang,” no. 060, 2002.
  14. D. P. Larasati, M. Nasrun, and U. A. Ahmad, “Analisis Dan Implementasi Algoritma Fp-Growth Pada Aplikasi Smart Untuk Menentukan Market Basket Analysis Pada Usaha Retail ( Studi Kasus : Pt . X ) Analysis and Implementation of Fp-Growth Algorithm in Smart Application To Determine Market Basket Analysi,” Sist. Komput., vol. 2, no. 1, pp. 749–755, 2015.
  15. K. Sumangkut, A. Lumenta, and V. Tulenan, “Analisa Pola Belanja Swalayan Daily Mart Untuk Menentukan Tata Letak Barang Menggunakan Algoritma FP-Growth,” Tek. Inform., vol. 8, no. 1, pp. 52–56, 2016.
  16. D. Fitrianah, A. N. Hidayanto, H. Fahmi, J. L. Gaol, and A. M. Arymurthy, “ST-AGRID : A Spatio Temporal Grid Density Based Clustering and,” Int. J. Softw. Eng. Its Appl., vol. 9, no. 1, pp. 13–26, 2015.
  17. A. Andriani, “Prediksi Kenaikan Rata-Rata Volume Perikanan Tangkap Dengan Teknik Data Mining ISBN : 979-26-0280-1 ISBN : 979-26-0280-1,” pp. 117–121, 2015.
  18. D. Fitrianah, “Feature Exploration for Prediction of Potential Tuna Fishing Zones,” Int. J. Inf. Electron. Eng., vol. 5, no. 4, pp. 270–274, 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining FP Growth Algorithm Association rules Fish Catch Rapid Miner.

Powered by PhDFocusTM